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  • An In Silico Glioblastoma Microenvironment Model Dissects the Immunological Mechanisms of Resistance to PD‐1 Checkpoint Blockade Immunotherapy
Title: An In Silico Glioblastoma Microenvironment Model Dissects the Immunological Mechanisms of Resistance to PD‐1 Checkpoint Blockade Immunotherapy
Abstract The PD‐1 immune checkpoint‐based therapy has emerged as a promising therapy strategy for treating the malignant brain tumor glioblastoma (GBM). However, patient response varies in clinical trials, mainly due to the tumor heterogeneity and immunological resistance in the tumor microenvironment. To further understand how mechanistically the niche interplay and competition drive anti‐PD‐1 resistance, an in silico model is established to quantitatively describe the biological rationale of critical GBM‐immune interactions, such as tumor growth and apoptosis, T cell activation and cytotoxicity, and tumor‐associated macrophage (TAM) mediated immunosuppression. Such an in silico experimentation and predictive model, based on the in vitro microfluidic chip‐measured end‐point data and patient‐specific immunological characteristics, allows for a comprehensive and dynamic analysis of multiple TAM‐associated immunosuppression mechanisms against the anti‐PD‐1 immunotherapy. The computational model demonstrates that the TAM‐associated immunosuppression varies in severity across different GBM subtypes, which results in distinct tumor responses. The prediction results indicate that a combination therapy by co‐targeting of PD‐1 checkpoint and TAM‐associated CSF‐1R signaling can enhance the immune responses of GBM patients, especially those patients with mesenchymal GBM who are irresponsive to the single anti‐PD‐1 therapy. The development of a patient‐specific in silico–in vitro GBM model will help navigate and personalize immunotherapies for GBM patients.  more » « less
Award ID(s):
1701322
PAR ID:
10450762
Author(s) / Creator(s):
 ;  ;  ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
Small Methods
Volume:
5
Issue:
6
ISSN:
2366-9608
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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